Student's t Test

Test difference between means of entire collection (EC) and core set (CS) for quantitative traits by Student's t test (Student 1908) .

ttest.evaluate.core(data, names, quantitative, selected)

Arguments

data: The data as a data frame object. The data frame should possess one row per individual and columns with the individual names and multiple trait/character data.
names: Name of column with the individual names as a character string
quantitative: Name of columns with the quantitative traits as a character vector.
selected: Character vector with the names of individuals selected in core collection and present in the names column.

Value

Trait: The quantitative trait.
EC_Min: The minimum value of the trait in EC.
EC_Max: The maximum value of the trait in EC.
EC_Mean: The mean value of the trait in EC.
EC_SE: The standard error of the trait in EC.
CS_Min: The minimum value of the trait in CS.
CS_Max: The maximum value of the trait in CS.
CS_Mean: The mean value of the trait in CS.
CS_SE: The standard error of the trait in CS.
ttest_pvalue: The p value of the Student's t test for equality of means of EC and CS.
ttest_significance: The significance of the Student's t test for equality of means of EC and CS.

References

Student (1908). “The probable error of a mean.” Biometrika, 6(1), 1--25.

Examples


data("cassava_CC")
data("cassava_EC")

ec <- cbind(genotypes = rownames(cassava_EC), cassava_EC)
ec$genotypes <- as.character(ec$genotypes)
rownames(ec) <- NULL

core <- rownames(cassava_CC)

quant <- c("NMSR", "TTRN", "TFWSR", "TTRW", "TFWSS", "TTSW", "TTPW", "AVPW",
           "ARSR", "SRDM")
qual <- c("CUAL", "LNGS", "PTLC", "DSTA", "LFRT", "LBTEF", "CBTR", "NMLB",
          "ANGB", "CUAL9M", "LVC9M", "TNPR9M", "PL9M", "STRP", "STRC",
          "PSTR")

ec[, qual] <- lapply(ec[, qual],
                     function(x) factor(as.factor(x)))

ttest.evaluate.core(data = ec, names = "genotypes",
                    quantitative = quant, selected = core)
#>    Trait EC_Min EC_Max   EC_Mean      EC_SE CS_Min CS_Max   CS_Mean     CS_SE
#> 1   NMSR   1.00  55.00 11.722090 0.18731651   1.00  55.00 10.892857 0.6314310
#> 2   TTRN   0.25  13.75  3.853909 0.04656232   0.25  13.75  3.930655 0.1754066
#> 3  TFWSR   0.00  40.00  5.428979 0.11045567   0.20  38.00  6.348214 0.4696134
#> 4   TTRW   0.00  20.20  1.897948 0.04037796   0.10  20.20  2.617817 0.2207393
#> 5  TFWSS   0.20  42.00  6.943052 0.14359496   0.20  42.00  7.748214 0.5588314
#> 6   TTSW   0.04  22.00  2.387502 0.04878843   0.10  22.00  3.069087 0.2381436
#> 7   TTPW   0.40  80.00 12.372031 0.23851356   0.40  80.00 14.096429 0.9720528
#> 8   AVPW   0.20  33.00  4.285450 0.08195995   0.20  33.00  5.686905 0.4210897
#> 9   ARSR   0.00  18.00  1.858076 0.05493488   0.00   8.00  1.702381 0.1516377
#> 10  SRDM   0.50  48.90 37.771021 0.12264395  21.90  48.10 37.730357 0.3289992
#>    ttest_pvalue ttest_significance
#> 1   0.209505833                 ns
#> 2   0.672855738                 ns
#> 3   0.058266745                 ns
#> 4   0.001585008                 **
#> 5   0.164507433                 ns
#> 6   0.005599998                 **
#> 7   0.086559633                 ns
#> 8   0.001302429                 **
#> 9   0.335457319                 ns
#> 10  0.907907091                 ns

Arguments

Value

References

See also

Examples